OpenRaiser/Intern-Atlas huggingface dataset:
GVFs in the Real World: Making Predictions Online for Water Treatment
Venue: arXiv cs.LG 2023
Year: 2023

In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a critical step on the path to optimizing and automating water treatment. Before that, there are many questions to answer about the predictability of the data, suitable neural network architectures, how to overcome partial observability and more. We first describe this dataset, and highlight challenges with seasonality, nonstationarity, partial observability, and heterogeneity across sensors and operation modes of the plant. We then describe General Value Function (GVF) predictions -- discounted cumulative sums of observations -- and highlight why they might be preferable to classical n-step predictions common in time series prediction. We discuss how to use offline data to appropriately pre-train our temporal difference learning (TD) agents that learn these GVF predictions, including how to select hyperparameters for online fine-tuning in deployment. We find that the TD-prediction agent obtains an overall lower normalized mean-squared error than the n-step prediction agent. Finally, we show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online. This final result is one of the first to motivate the importance of adapting predictions in real-time, for non-stationary high-volume systems in the real world.
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
Venue: arXiv cs.CV 2023
Year: 2023

Image Style Transfer (IST) is an interdisciplinary topic of computer vision and art that continuously attracts researchers' interests. Different from traditional Image-guided Image Style Transfer (IIST) methods that require a style reference image as input to define the desired style, recent works start to tackle the problem in a text-guided manner, i.e., Text-guided Image Style Transfer (TIST). Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images. Unfortunately, many TIST approaches produce undesirable artifacts in the transferred images. To address this issue, we present a novel method to achieve much improved style transfer based on text guidance. Meanwhile, to offer more flexibility than IIST and TIST, our method allows style inputs from multiple sources and modalities, enabling MultiModality-guided Image Style Transfer (MMIST). Specifically, we realize MMIST with a novel cross-modal GAN inversion method, which generates style representations consistent with specified styles. Such style representations facilitate style transfer and in principle generalize any IIST methods to MMIST. Large-scale experiments and user studies demonstrate that our method achieves state-of-the-art performance on TIST task. Furthermore, comprehensive qualitative results confirm the effectiveness of our method on MMIST task and cross-modal style interpolation.
STADEE: STAtistics-based DEEp Detection of Machine Generated Text
Venue: arXiv cs.CL 2023
Year: 2023

We present STADEE, a \textbf{STA}tistics-based \textbf{DEE}p detection method to identify machine-generated text, addressing the limitations of current methods that rely heavily on fine-tuning pre-trained language models (PLMs). STADEE integrates key statistical text features with a deep classifier, focusing on aspects like token probability and cumulative probability, crucial for handling nucleus sampling. Tested across diverse datasets and scenarios (in-domain, out-of-domain, and in-the-wild), STADEE demonstrates superior performance, achieving an 87.05% F1 score in-domain and outperforming both traditional statistical methods and fine-tuned PLMs, especially in out-of-domain and in-the-wild settings, highlighting its effectiveness and generalizability.
Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms
Venue: arXiv cs.LG 2023
Year: 2023

Customizing services for bus travel can bolster its attractiveness, optimize usage, alleviate traffic congestion, and diminish carbon emissions. This potential is realized by harnessing recent advancements in positioning communication facilities, the Internet of Things, and artificial intelligence for feature mining in public transportation. However, the inherent complexities of disorganized and unstructured public transportation data introduce substantial challenges to travel feature extraction. This study presents a bus travel feature extraction method rooted in Point of Interest (POI) data, employing enhanced P-KMENAS and P-LDA algorithms to overcome these limitations. While the KMEANS algorithm adeptly segments passenger travel paths into distinct clusters, its outcomes can be influenced by the initial K value. On the other hand, Latent Dirichlet Allocation (LDA) excels at feature identification and probabilistic interpretations yet encounters difficulties with feature intermingling and nuanced sub-feature interactions. Incorporating the POI dimension enhances our understanding of travel behavior, aligning it more closely with passenger attributes and facilitating easier data analysis. By incorporating POI data, our refined P-KMENAS and P-LDA algorithms grant a holistic insight into travel behaviors and attributes, effectively mitigating the limitations above. Consequently, this POI-centric algorithm effectively amalgamates diverse POI attributes, delineates varied travel contexts, and imparts probabilistic metrics to feature properties. Our method successfully mines the diverse aspects of bus travel, such as age, occupation, gender, sports, cost, safety, and personality traits. It effectively calculates relationships between individual travel behaviors and assigns explanatory and evaluative probabilities to POI labels, thereby enhancing bus travel optimization.
Open-DDVM: A Reproduction and Extension of Diffusion Model for Optical Flow Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Recently, Google proposes DDVM which for the first time demonstrates that a general diffusion model for image-to-image translation task works impressively well on optical flow estimation task without any specific designs like RAFT. However, DDVM is still a closed-source model with the expensive and private Palette-style pretraining. In this technical report, we present the first open-source DDVM by reproducing it. We study several design choices and find those important ones. By training on 40k public data with 4 GPUs, our reproduction achieves comparable performance to the closed-source DDVM. The code and model have been released in https://github.com/DQiaole/FlowDiffusion_pytorch.
Deep CNN for Coherent Seismic Noise Removal: A Perspective
Venue: arXiv cs.CE 2023
Year: 2023

Seismic denoising is an important processing step before subsequent imaging and interpretation, which consumes a significant amount of time, whether it is for Quality control or for the associated computations. We present results of our work in training convolutional neural networks for denoising seismic data, specifically attenuation of surface related multiples and removal of overlap of shot energies during simultaneous-shooting survey. The proposed methodology is being explored not only for its ability to minimize human involvement but also because of the trained filter's ability to accelerate the process, hence, reduce processing time.
A Comprehensive Literature Review on Sweet Orange Leaf Diseases
Venue: arXiv cs.CV 2023
Year: 2023

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies
Faster Sublinear-Time Edit Distance
Venue: arXiv cs.DS 2023
Year: 2023

We study the fundamental problem of approximating the edit distance of two strings. After an extensive line of research led to the development of a constant-factor approximation algorithm in almost-linear time, recent years have witnessed a notable shift in focus towards sublinear-time algorithms. Here, the task is typically formalized as the $(k, K)$-gap edit distance problem: Distinguish whether the edit distance of two strings is at most $k$ or more than $K$. Surprisingly, it is still possible to compute meaningful approximations in this challenging regime. Nevertheless, in almost all previous work, truly sublinear running time of $O(n^{1-\varepsilon})$ (for a constant $\varepsilon > 0$) comes at the price of at least polynomial gap $K \ge k \cdot n^{Ω(\varepsilon)}$. Only recently, [Bringmann, Cassis, Fischer, and Nakos; STOC'22] broke through this barrier and solved the sub-polynomial $(k, k^{1+o(1)})$-gap edit distance problem in time $O(n/k + k^{4+o(1)})$, which is truly sublinear if $n^{Ω(1)} \le k \le n^{\frac14-Ω(1)}$.The $n/k$ term is inevitable (already for Hamming distance), but it remains an important task to optimize the $\mathrm{poly}(k)$ term and, in general, solve the $(k, k^{1+o(1)})$-gap edit distance problem in sublinear-time for larger values of $k$. In this work, we design an improved algorithm for the $(k, k^{1+o(1)})$-gap edit distance problem in sublinear time $O(n/k + k^{2+o(1)})$, yielding a significant quadratic speed-up over the previous $O(n/k + k^{4+o(1)})$-time algorithm. Notably, our algorithm is unconditionally almost-optimal (up to subpolynomial factors) in the regime where $k \leq n^{\frac13}$ and improves upon the state of the art for $k \leq n^{\frac12-o(1)}$.
Viral transmission in pedestrian crowds: Coupling an open-source code assessing the risks of airborne contagion with diverse pedestrian dynamics models
Venue: arXiv cs.MA 2023
Year: 2023

We study viral transmission in crowds via the short-ranged airborne pathway using a purely model-based approach. Our goal is two-pronged. Firstly, we illustrate with a concrete and pedagogical case study how to estimate the risks of new viral infections by coupling pedestrian simulations with the transmission algorithm that we recently released as open-source code. The algorithm hinges on pre-computed viral concentration maps derived from computational fluid dynamics (CFD) simulations. Secondly, we investigate to what extent the transmission risk predictions depend on the pedestrian dynamics model in use. For the simple bidirectional flow under consideration, the predictions are found to be surprisingly stable across initial conditions and models, despite the different microscopic arrangements of the simulated crowd, as long as the crowd evolves in a qualitatively similarly way. On the other hand, when major changes are observed in the crowd's behaviour, notably whenever a jam occurs at the centre of the channel, the estimated risks surge drastically.
Distributed Continual Learning with CoCoA in High-dimensional Linear Regression
Venue: arXiv cs.LG 2023
Year: 2023

We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time. In particular, we consider the continual learning problem where different tasks, e.g., data with different distributions, arrive sequentially and the aim is to perform well on the newly arrived task without performance degradation on the previously seen tasks. In contrast to the continual learning literature focusing on the centralized setting, we investigate the problem from a distributed estimation perspective. We consider the well-established distributed learning algorithm COCOA, which distributes the model parameters and the corresponding features over the network. We provide exact analytical characterization for the generalization error of COCOA under continual learning for linear regression in a range of scenarios, where overparameterization is of particular interest. These analytical results characterize how the generalization error depends on the network structure, the task similarity and the number of tasks, and show how these dependencies are intertwined. In particular, our results show that the generalization error can be significantly reduced by adjusting the network size, where the most favorable network size depends on task similarity and the number of tasks. We present numerical results verifying the theoretical analysis and illustrate the continual learning performance of COCOA with a digit classification task.
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
Venue: arXiv cs.CL 2023
Year: 2023

The potential of using a large language model (LLM) as a knowledge base (KB) has sparked significant interest. To manage the knowledge acquired by LLMs, we need to ensure that the editing of learned facts respects internal logical constraints, which are known as dependency of knowledge. Existing work on editing LLMs has partially addressed the issue of dependency, when the editing of a fact should apply to its lexical variations without disrupting irrelevant ones. However, they neglect the dependency between a fact and its logical implications. We propose an evaluation protocol with an accompanying question-answering dataset, DepEdit, that provides a comprehensive assessment of the editing process considering the above notions of dependency. Our protocol involves setting up a controlled environment in which we edit facts and monitor their impact on LLMs, along with their implications based on If-Then rules. Extensive experiments on DepEdit show that existing knowledge editing methods are sensitive to the surface form of knowledge, and that they have limited performance in inferring the implications of edited facts.
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Venue: arXiv cs.CV 2023
Year: 2023

Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited to a small number of pathologies where there is good availability of labeled data. In contrast, unsupervised anomaly detection (UAD) has the potential to identify a broader spectrum of anomalies by spotting deviations from normal patterns. Our research demonstrates that existing state-of-the-art UAD approaches do not generalise well to diverse types of anomalies in realistic multi-modal MR data. To overcome this, we introduce a new UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by aggregating differences between predicted denoising steps and ground truth backwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that have been trained on pyramidal Gaussian noise. We validate ANDi against three recent UAD baselines, and across three diverse brain MRI datasets. We show that ANDi, in some cases, substantially surpasses these baselines and shows increased robustness to varying types of anomalies. Particularly in detecting multiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in terms of AUPRC.
Instance-guided Cartoon Editing with a Large-scale Dataset
Venue: arXiv cs.CV 2023
Year: 2023

Cartoon editing, appreciated by both professional illustrators and hobbyists, allows extensive creative freedom and the development of original narratives within the cartoon domain. However, the existing literature on cartoon editing is complex and leans heavily on manual operations, owing to the challenge of automatic identification of individual character instances. Therefore, an automated segmentation of these elements becomes imperative to facilitate a variety of cartoon editing applications such as visual style editing, motion decomposition and transfer, and the computation of stereoscopic depths for an enriched visual experience. Unfortunately, most current segmentation methods are designed for natural photographs, failing to recognize from the intricate aesthetics of cartoon subjects, thus lowering segmentation quality. The major challenge stems from two key shortcomings: the rarity of high-quality cartoon dedicated datasets and the absence of competent models for high-resolution instance extraction on cartoons. To address this, we introduce a high-quality dataset of over 100k paired high-resolution cartoon images and their instance labeling masks. We also present an instance-aware image segmentation model that can generate accurate, high-resolution segmentation masks for characters in cartoon images. We present that the proposed approach enables a range of segmentation-dependent cartoon editing applications like 3D Ken Burns parallax effects, text-guided cartoon style editing, and puppet animation from illustrations and manga.
Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs.
Augmenting Channel Charting with Classical Wireless Source Localization Techniques
Venue: arXiv cs.IT 2023
Year: 2023

Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting channel charts usually accurately represent local neighborhood relationships, even under conditions with strong multipath propagation, they often fall short in capturing global geometric features. On the other hand, classical model-based localization methods, such as triangulation and multilateration, can easily localize signal sources in the global coordinate frame. However, these methods rely heavily on the assumption of line-of-sight channels and distributed antenna deployments. Based on measured data, we compare classical source localization techniques to channel charts with respect to localization performance. We suggest and evaluate methods to enhance Channel Charting with model-based localization approaches: One approach involves using information derived from classical localization methods to map channel chart locations to physical positions after conventional training of the forward charting function. Foremost, though, we suggest to incorporate information from model-based approaches during the training of the forward charting function in what we call "augmented Channel Charting". We demonstrate that Channel Charting can outperform classical localization methods on the considered dataset.
Computing Repairs Under Functional and Inclusion Dependencies via Argumentation
Venue: arXiv cs.CC 2023
Year: 2023

We discover a connection between finding subset-maximal repairs for sets of functional and inclusion dependencies, and computing extensions within argumentation frameworks (AFs). We study the complexity of the existence of a repair and deciding whether a given tuple belongs to some (or every) repair, by simulating the instances of these problems via AFs. We prove that subset-maximal repairs under functional dependencies correspond to the naive extensions, which also coincide with the preferred and stable extensions in the resulting AFs. For inclusion dependencies, one needs a pre-processing step on the resulting AFs in order for the extensions to coincide. Allowing both types of dependencies breaks this relationship between extensions, and only preferred semantics captures the repairs. Finally, we establish that the complexities of the above decision problems are NP-complete and Pi_2^P-complete, when both functional and inclusion dependencies are allowed.
Federated Learning is Better with Non-Homomorphic Encryption
Venue: arXiv cs.CR 2023
Year: 2023

Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. To resolve these issues, we propose an innovative framework that synergizes permutation-based compressors with Classical Cryptography, even though employing Classical Cryptography was assumed to be impossible in the past in the context of FL. Our framework offers a way to replace HE with cheaper Classical Cryptography primitives which provides security for the training process. It fosters asynchronous communication and provides flexible deployment options in various communication topologies.
Transitions of Solutions and Their Efficiency
Venue: arXiv cs.GT 2023
Year: 2023

We broaden the basis of non-cooperative game theory by considering miscoordination on a solution concept. For any solution concept, we extend the solution set of a strategic-form game to a transition set. This set contains profiles where various agents simultaneously follow different solutions, e.g.~different Nash equilibria. This models the fact that in practice, complicated agents are rarely perfectly coordinated on the same equilibrium. We define two efficiency measures, called the price of transition anarchy and stability, and bound them. We also refine the notion of transition to the notion of limited transition, where only a limited number of solutions is simultaneously played, and to stable transitions, which allow for only minor lack of coordination. We compare the above mentioned efficiency measures and bound the efficiency of transitions in important cases, including the important cases of constant-sum and potential games, which span the set of finite games with the same number of strategies for each agent. We also prove tight efficiency bounds for routing games and coordination games on graphs. Finally, we study algorithms to find the transition degree required to make a given profile a transition, or to render all the profiles transitions. We conclude that for the sake of efficiency, it is crucial to avoid uncoordinated transitions, besides certain cases, such as constant-sum games, identical utility games, some types of routing games, limited transitions in potential games, and stable transitions in coordination games.